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In my humble opinion all discovery algorithms with all large providers are terrible. Instead of expanding they narrow down choices and never surprise me in a good way like some human curated playlist can.

I had been switching between Apple Music, Spotify and Google Music in different countries. Currently I have settled on Google Music simply because Google practically abandoned Google Music so they are not so in my face with curated playlists and stations.

On the other hand I think that there is opportunity for good (like in really working) AI engine for music recommendations.



The unofficial gmusic API is why I continue using gmusic. I make many playlists for work (teaching spin classes) and I use the API to aggregate the songs I use and clean out old playlists.


What’s interesting is that these algos are trained on human feedback. What does that say about our revealed preferences in music?


If you let an algorithm script a movie, it would be a 2-hr superhero movie with extremely simplistic moral conundrums, that will cost $300m to make, but will gross $1 billion +.

If the algo scripted a TV sitcom, it would be 5-6 mostly white people who occasionally hook up, break up, get married, struggle to conceive, and then everything eventually works out.

Same goes for music. People have a habit of liking what's in front of them. Few are going to want to make the effort to find something that's more than "good enough".


As an anecdote I have left Apple Music after it had been suggesting to me at least 3 heavy metal playlists for 30 days. And this is the one genre I do not listen to.

I had been clicking 'I don't like it' buttons hoping the AI will get it but no luck. So after 30 days I had enough and moved on back to Spotify.

So much for human feedback.


Maybe you actually like heavy metal but won’t admit it to yourself ;).

(Just kidding, but this kind of thing will be more common in the future, especially as we learn more about how little the brain knows about its own desires and motivations)




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